2-plot-figure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a0d09ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob, os, h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# PLOTTING OPTIONS\n",
    "fig_width_pt = 3*246.0  # Get this from LaTeX using \\showthe\\columnwidth\n",
    "inches_per_pt = 1.0/72.27               # Convert pt to inch\n",
    "golden_mean = (np.sqrt(5)-1.0)/2.0         # Aesthetic ratio\n",
    "fig_width = fig_width_pt*inches_per_pt  # width in inches\n",
    "fig_height = fig_width*golden_mean      # height in inches\n",
    "fig_size =  [fig_width,fig_height]\n",
    "\n",
    "params = { 'axes.labelsize': 24,\n",
    "          'font.family': 'serif',\n",
    "          'font.serif': 'Computer Modern Raman',\n",
    "          'font.size': 24,\n",
    "          'legend.fontsize': 20,\n",
    "          'xtick.labelsize': 24,\n",
    "          'ytick.labelsize': 24,\n",
    "          'axes.grid' : True,\n",
    "          'text.usetex': True,\n",
    "          'savefig.dpi' : 100,\n",
    "          'lines.markersize' : 14,\n",
    "          'figure.figsize': fig_size}\n",
    "\n",
    "mpl.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04eb4ccb",
   "metadata": {},
   "source": [
    "# Reading the file directly from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48eef794",
   "metadata": {},
   "outputs": [],
   "source": [
    "fg = pd.read_csv('candidate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a56b5fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "bg = pd.read_csv('background.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49922414",
   "metadata": {},
   "source": [
    "# Making a figure for the paper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af453ff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of events / Obs')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 735.243x454.405 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "bgranking = bg['ranking']\n",
    "fgranking = fg['ranking']\n",
    "\n",
    "low=-30\n",
    "bins = np.linspace(low,np.max(bgranking)+1,200)\n",
    "counts, _ = np.histogram(bgranking,bins=bins)\n",
    "\n",
    "ax.hist(bins[:-1], bins, weights=counts/100,histtype=\"step\",label='background')\n",
    "ax.hist(fgranking,bins=bins,alpha=0.7,color='grey',label='candidates')\n",
    "ax.set_yscale('log')\n",
    "ax.legend()\n",
    "ax.set_xlim(-30,35)\n",
    "ax.set_xlabel('$\\lambda_\\mathrm{gw+frb}$')\n",
    "ax.set_ylabel('Number of events / Obs')\n",
    "#fig.savefig('newstat.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b71a11b0",
   "metadata": {},
   "source": [
    "# FAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58754a6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat: 8.922940943390131\n",
      "0.29\n",
      "stat: 7.506948210299015\n",
      "0.63\n",
      "stat: 6.950599300907924\n",
      "0.93\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print('stat:', fg['ranking'].values[i])\n",
    "    higherbg = bg[bg['ranking']>=fg['ranking'].values[i]]\n",
    "    print( len (higherbg) / 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "340602dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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